Managing supply chain resources with Big Data Analytics: a systematic review

被引:82
作者
Barbosa, Marcelo Werneck [1 ,2 ]
de la Calle Vicente, Alberto [3 ]
Ladeira, Marcelo Bronzo [1 ]
Valadares de Oliveira, Marcos Paulo [4 ]
机构
[1] Univ Fed Minas Gerais, Dept Adm, Belo Horizonte, MG, Brazil
[2] Pontificia Univ Catolica Minas Gerais, Dept Software Engn & Informat Syst, Belo Horizonte, MG, Brazil
[3] Univ Deusto, Dept Ind Technol, Bilbao, Spain
[4] Univ Fed Espirito Santo, Dept Adm, Vitoria, Brazil
关键词
Big Data Analytics; Business Analytics; Supply Chain Analytics; Supply Chain Intelligence; supply chain management; Resource-based View; BUSINESS INTELLIGENCE; PREDICTIVE ANALYTICS; KNOWLEDGE MANAGEMENT; DYNAMIC-CAPABILITIES; DATA INITIATIVES; DECISION-MAKING; DATA SCIENCE; PERFORMANCE; CHALLENGES; INTEGRATION;
D O I
10.1080/13675567.2017.1369501
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
Big Data Analytics (BDA) has the potential to improve demand forecasting, communications and better manage supply chain resources. Despite such recognised benefits and the increase of BDA research, little is known about the general approaches used to investigate BDA in the context of supply chain management (SCM). In the light of the Resource-based View, the main goal of this study was, by means of a systematic literature review, to comprehend how BDA has been investigated on SCM studies, which resources are managed by BDA as well as which SCM processes are involved. Our study found out that the predictive and prescriptive approaches are more frequently used and organisational, technological and human resources are often managed by BDA. It was observed a focus on Demand Management and Order Fulfilment processes and a lack of studies on Returns Management, which indicates an open research area that should be exploited by future studies.
引用
收藏
页码:177 / 200
页数:24
相关论文
共 50 条
  • [21] Big data analytics in operations and supply chain management
    Wamba, Samuel Fosso
    Gunasekaran, Angappa
    Dubey, Rameshwar
    Ngai, Eric W. T.
    [J]. ANNALS OF OPERATIONS RESEARCH, 2018, 270 (1-2) : 1 - 4
  • [22] Role of Big Data Analytics in supply chain management: current trends and future perspectives
    Maheshwari, Sumit
    Gautam, Prerna
    Jaggi, Chandra K.
    [J]. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2021, 59 (06) : 1875 - 1900
  • [23] Big data analytics in supply chain management between 2010 and 2016: Insights to industries
    Tiwari, Sunil
    Wee, H. M.
    Daryanto, Yosef
    [J]. COMPUTERS & INDUSTRIAL ENGINEERING, 2018, 115 : 319 - 330
  • [24] Big Data and supply chain management: a review and bibliometric analysis
    Mishra, Deepa
    Gunasekaran, Angappa
    Papadopoulos, Thanos
    Childe, Stephen J.
    [J]. ANNALS OF OPERATIONS RESEARCH, 2018, 270 (1-2) : 313 - 336
  • [25] Critical analysis of the impact of big data analytics on supply chain operations
    Hasan, Ruaa
    Kamal, Muhammad Mustafa
    Daowd, Ahmad
    Eldabi, Tillal
    Koliousis, Ioannis
    Papadopoulos, Thanos
    [J]. PRODUCTION PLANNING & CONTROL, 2024, 35 (01) : 46 - 70
  • [26] Effect of Big Data Analytics in Reverse Supply Chain: An Indian Context
    Behera, Ajay Kumar
    Mohapatra, Sasmita
    Mahapatra, Rabindra
    Das, Harish
    [J]. INTERNATIONAL JOURNAL OF INFORMATION SYSTEMS AND SUPPLY CHAIN MANAGEMENT, 2022, 15 (01)
  • [27] Big data analytics in sustainable humanitarian supply chain: barriers and their interactions
    Bag, Surajit
    Gupta, Shivam
    Wood, Lincoln
    [J]. ANNALS OF OPERATIONS RESEARCH, 2020, 319 (1) : 721 - 760
  • [28] Adoption of Big Data Analytics in Supply Chain Management: Combining Organizational Factors With Supply Chain Connectivity
    Alsadi, Amin Khalil
    Alaskar, Thamir Hamad
    Mezghani, Karim
    [J]. INTERNATIONAL JOURNAL OF INFORMATION SYSTEMS AND SUPPLY CHAIN MANAGEMENT, 2021, 14 (02) : 88 - 107
  • [29] Big Data Analytics for Supply Chain Innovation
    Singh, Mabeena
    Chennamaneni, Anitha
    [J]. AMCIS 2016 PROCEEDINGS, 2016,
  • [30] Big data analytics in supply chain management: A state-of-the-art literature review
    Truong Nguyen
    Zhou, Li
    Spiegler, Virginia
    Ieromonachou, Petros
    Lin, Yong
    [J]. COMPUTERS & OPERATIONS RESEARCH, 2018, 98 : 254 - 264